• Data scientist and certified machine learning engineer based in Southern California
• MS information science; BS psychology; 2nd major linguistics; minor cognitive science
• English, Mandarin Chinese, Spanish

I'm a lifelong learner and always curious. I am a full-stack machine learning developer with practical experience in every stage of the data lifecycle.

After working in bioinformatics at a virology lab, I earned a master's from one of the highest-ranking information schools in the US and traveled to research and write about global airport cities and aerotropolises. To deepen my expertise in data science and artificial intelligence, I completed Springboard's intensive machine learning engineering certification program.

My experiences in many fields of work and study enable a multifaceted approach to mastering new concepts and solving problems in a variety of contexts:

• Formal education provided a solid foundation in academic writing, critical thinking, quantitative methods, and human subjects research.
• As a CEO's executive assistant, I practiced creative problem-solving.
• Customer service roles cultivated patience.
• Self-employment honed my grit and resilience.
• Logistics and moving companies taught me how it feels to work with my hands.
• Multinational collaboration developed cultural awareness.
• Sales and office roles have further nurtured my people skills.

Business is about people. People make decisions — not companies. So let's guide our decisions with evidence and data. Hit me up and we can talk more.

Contact me directly at [email protected].

Outside work, I enjoy cats, k-pop, fitness, martial arts, music, nature, travel, and video games.

• Languages: Python, SQL, JavaScript, SAS, Java
• Libraries and frameworks: Scikit-learn, statsmodels, TensorFlow, PySpark, Pandas,
• NumPy, SciPy, Matplotlib, Seaborn, pytest, Streamlit
• Other tools: Jupyter Lab, Jupyter Notebook, Visual Studio Code, PowerShell, Git,
• Sublime, Travis CI, Excel, Windows Subsystem for Linux

Keywords: Classification, Clustering, Data Analysis, Data Cleaning, Data Visualization, Data Wrangling, Deep Learning, Experimental Design, Exploratory Data Analysis (EDA), Hyperparameter Optimization, JSON, Neural Networks, Optimisation, Predictive Modelling, Regression, Statistical Modeling, Statistics, Supervised Learning, Unsupervised Learning, Visualisation.